Journal of CENTRUM Cathedra

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    Productivity of Automobile Industries using the Malmquist Index: Evidence from the Last Economic Recession
    (Pontificia Universidad Católica del Perú. CENTRUM, 2011) Chen, Yao
    In this paper, the financial information provided by Fortune Magazine is used to study the productivity changes in the global auto industry during 1991-1997, including automakers from the USA, Europe, Japan, and South Korean. The paper seeks to uncover global auto industry’s productivity changes during the early 1990s economic recession. Data envelopment analysis (DEA) is used to identify the empirical performance frontier. A new DEA-based Malmquist productivity index is used to further analyze the two Malmquist components. The analysis not only reveals patterns of productivity change and presents a new interpretation along with the managerial implications of each Malmquist component, but also the analysis identifies the strategy shifts of individual companies based upon isoquant changes. The labor efficiency and overcapacity of the global auto industry is studied, and judgments can be made about whether such strategy shifts are favorable and promising.
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    Benchmarking Peruvian Banks using Data Envelopment Analysis
    (Pontificia Universidad Católica del Perú. CENTRUM, 2011) Charles, Vincent; Kumar, Mukesh; Zegarra, Luis Felipe; Avolio, Beatrice
    Despite the growing literature on bank efficiency worldwide over the last decade, researchers have neglected the Peruvian banking sector. In this paper, the technique of data envelopment analysis (DEA) is used to investigate the efficiency of Peruvian banks for the period 2000 to 2009 to benchmark currently existing banks based on their super-efficiency scores over time. Further, an in-depth analysis of currently existing banks for the period 2008 to 2009 is conducted to check the robustness of DEA efficiency scores and the potential improvement of inputs and outputs for inefficient banks, indicating by how much and in what areas inefficient banks need to improve in order to be efficient. Our finding shows an increasing trend in technical efficiency during the period 2000 to 2009 which gives an indication of an affirmative effect of the reform process in the Peruvian banking sector. On average, the multinational banks are performing better than are domestic banks throughout the period except in 2007, during which a sharp decline in efficiency performance for both the groups was apparent, possibly a result of global financial turmoil. The application of jackknifing analysis with appropriate statistical tools shows the DEA efficiency scores are robust. Among the 14 currently existing banks, Banco Ripley and Banco Santander Peru were the best performers, whereas Banco Azteca was the worst performer, followed by Interbank and Banco de Comercio. Furthermore our findings suggest that inefficient banks require more rigorous policies with respect to the allocation of funds for additional loans as well as other earnings assets. In this way, presently inefficient banks may approach the efficiencies of the best practice banks.
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    Natural Resources Exchange Traded Funds: Performance Appraisal using DEA Modeling
    (Pontificia Universidad Católica del Perú. CENTRUM, 2011) Tsolas, I. E.
    The purpose of this paper is to evaluate the performance of a sample of natural resources exchange traded funds (ETFs) by applying a two-stage procedure. In the first stage, the generalized proportional distance function (GPDF) in the data envelopment analysis (DEA) context is used for the first time to measure the relative efficiency of sectoral ETFs. In the second stage, a Tobit model is employed to identify the drivers of performance. The results indicate there is scope for efficiency improvement for about half or more of the sample funds depending on the variables used in the assessments, and fund performance can be explained by fund persistence and the beta coefficient.
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    Scale Efficiency Measurement in Data Envelopment Analysis with Interval Data: A Two-Level Programming Approach
    (Pontificia Universidad Católica del Perú. CENTRUM, 2011) Kao, Chiang; Lu, Shiang-Tai
    Conventional data envelopment analysis (DEA) for measuring the relative efficiency of a set of decision-making units (DMUs) requires the observations to have precise values. When observations are imprecise and represented by interval values, the efficiencies are also expected to reflect interval values. Several methods exist to calculate the interval overall and technical efficiencies, but such methods are unable to calculate the interval scale efficiency. The focus of this paper is the application of a two-level programming technique to formulate the problem of determining the bounds of the interval scale efficiency. The associated models are essentially nonlinear programs with only bound constraints for variables in a sophisticated form. Hence, one can modify the conventional quasi-Newton method for unconstrained nonlinear programming problems to solve the two-level programs. Two examples with interval data, one hypothetical and one real, aid in explaining the proposed method and the properties of the results.
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    Airlines Performance via Two-Stage Network DEA Approach
    (Pontificia Universidad Católica del Perú. CENTRUM, 2011) Zhu, Joe
    The performance of the airline industry has been widely studied using data envelopment analysis (DEA). Many existing DEA-based airline performance studies have used the standard DEA model, with some minor modifications. These studies have ignored the internal structure relative to the measures characterizing airline operations performance. In the current paper, airline performance is measured using a two-stage process. In the first stage, resources (fuel, salaries, and other factors) are used to maintain the fleet size and load factor. In the second stage, the fleet size and load factors generate revenue. The model used is called the centralized efficiency model where two stages are used to optimize performance simultaneously. The approach generates efficiency decomposition for the two individual stages. The use of this centralized DEA model enables obtaining insights not available from the standard DEA approach.